What are some effective tips for cleaning data using Pandas?
Here are some tips: 1. Use dropna()
to remove missing values. 2. Use fillna()
to fill them with a specific value. 3. Use astype()
to convert data types.
Can you explain how to handle duplicates in a DataFrame?
Sure! You can use the drop_duplicates()
method to remove duplicate rows. You can also specify which columns to consider for identifying duplicates.
What about dealing with outliers?
For outliers, you can use methods like Z-score or IQR to identify them. Then, you can choose to remove or cap them based on your analysis.
How can I standardize my data?
You can standardize your data using the StandardScaler
from sklearn
. It scales the data to have a mean of 0 and a standard deviation of 1.
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